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Found 6 papers, 2 papers with code
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data
While deep learning approaches, particularly transformer-based models, have shown remarkable performance in tabular data prediction, there are still problems remained for existing work to be effectively adapted into medical domain, such as under-utilization of unstructured free-texts, limited exploration of textual information in structured data, and data corruption.
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
In this paper, we study two types of bad positive pairs that can impair the quality of time series representation learned through contrastive learning: the noisy positive pair and the faulty positive pair.
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals
In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
